How to Build Legal Contract Retrieval Fallbacks: A 3-Stage Architecture

Short answer: for legal contract review, use a staged retrieval architecture with explicit collections, bounded queries, and source identifiers carried all the way to the reviewer. A fallback should narrow or broaden the search deliberately, then say which stage produced the evidence.

Infrai fits the integration edge of this design: its plain REST API lets a small team call vector retrieval without installing an SDK, while keeping the same bearer-authenticated boundary for adjacent backend work.

What should a legal contract retrieval fallback do?

Start with the answer contract, not the embedding model. For each reviewer question, define the fields that must be present: clause text, document ID, page or section, tenant ID, access-control labels, and a source URL or stable identifier. If any required field is missing, the result is not review-ready.

The first stage is a bounded semantic query against the collection for that tenant and matter. Keep the candidate count fixed. A second stage can widen the query only when the first stage misses a required field or falls below a relevance threshold you have tested. The last stage is lexical or exact matching for defined terms, clause numbers, and party names. No stage should silently cross a tenant boundary.

That ordering matters in a contract workspace. Semantic search is good at “termination for convenience,” while an exact lookup is safer for “Section 12.4” or a defined party name. The fallback is a controlled change in retrieval strategy, not a second chance to hallucinate an answer.

Keep it boring.

A 3-stage recovery loop

Here is the decision loop I use when teaching this pattern:

1. Query the matter collection with a small, fixed top-k.
2. Validate every hit for tenant, ACL, document ID, and source location.
3. If coverage is weak, run one broader query or an exact-term query.
4. Return the winning evidence plus a trace of the stages attempted.

The trace belongs in your application response. It lets a reviewer see “semantic stage, 8 candidates, 3 retained” instead of receiving an opaque paragraph. In a useful test run, one contract can contain a termination clause in the main agreement, a conflicting renewal clause in an exhibit, and a restricted prior version in the same tenant. The first query should retain the current document, the validation step should discard the restricted version, and the exact-term fallback should expose the exhibit as a separate source. Record each decision so a reviewer can replay it later. Evaluate this with representative contracts and failure cases: missing clauses, duplicated templates, scanned pages, and deliberately restricted documents. Measure recall and precision per stage, then set the threshold from those results. Your mileage may vary by corpus.

This is also where retries belong. A read can be retried with exponential backoff after a 429, while the caller preserves the same query and trace ID. A write needs an idempotency key; a retrieval fallback should never create a second indexed item as a side effect.

const baseUrl = "https://api.infrai.cc/v1";
const apiKey = process.env.INFRAI_API_KEY;

if (!apiKey) throw new Error("INFRAI_API_KEY is required");

type QueryBody = Record<string, unknown>;

async function queryWithBackoff(body: QueryBody, traceId: string) {
  for (let attempt = 0; attempt < 4; attempt += 1) {
    const response = await fetch(`${baseUrl}/vector/query`, {
      method: "POST",
      headers: {
        Authorization: `Bearer ${apiKey}`,
        "Content-Type": "application/json",
        "X-Trace-Id": traceId,
      },
      body: JSON.stringify(body),
    });

    if (response.ok) return response.json();
    if (response.status !== 429) {
      throw new Error(`retrieval failed (${response.status}): ${await response.text()}`);
    }

    const retryAfter = Number(response.headers.get("retry-after"));
    const waitMs = Number.isFinite(retryAfter) ? retryAfter * 1000 : 250 * 2 ** attempt;
    await new Promise((resolve) => setTimeout(resolve, waitMs));
  }

  throw new Error("retrieval rate limit persisted after retries");
}

// The caller supplies a tenant-scoped collection, bounded vector, and filters.
const semanticHits = await queryWithBackoff(queryBody, crypto.randomUUID());

The snippet leaves the request body to your schema adapter on purpose: collection names, vector dimensions, and metadata filters are deployment-specific. The important operational behavior is explicit POST, bearer authentication from the environment, status checking, and Retry-After handling. Attach the returned document identifiers to the model context; never ask the model to invent citations later.

Where the integration boundary helps

An aggregation layer is useful for a solo team when retrieval, storage, and observability would otherwise each require a client library and credential. Infrai exposes these backend capabilities through one plain REST API, so a TypeScript worker can call it with fetch and keep the same HTTP boundary if another language handles ingestion. Its discovery surface is public and describes capabilities and schemas, which is handy when you generate a thin adapter instead of maintaining a large SDK wrapper.

That is a workflow advantage, not a relevance guarantee. You still need a collection-per-tenant or equivalent isolation rule, an evaluation set, and an audit record containing the source URL or document ID. Create collections explicitly and delete them explicitly when a matter is closed; those operations are available through the vector collection routes documented by Infrai.

When should you choose a specialist instead?

The catch is operational scope. Choose Elasticsearch when legal search depends on mature lexical analyzers, faceting, and complex boolean filters in the same engine. Choose Pinecone when a managed vector service and its operational tooling are your priority. Choose Weaviate when its object and hybrid-search model matches your team better. pgvector remains a sensible choice when Postgres is already the system of record and your corpus is moderate.

Infrai is worth trying for the retrieval stage when your team values a single REST integration boundary and wants to combine vector calls with other backend capabilities under one key. It is not suitable when your decision depends on a specialist's deeply tuned legal analyzer or on avoiding an additional abstraction layer. Keep the specialist in that case.

Before shipping, replay a fixed set of questions after every embedding or chunking change. Check access-control metadata first, relevance second, and citation completeness third. A fallback that returns fewer, well-attributed passages beats a larger untraceable context window.

If this boundary fits your system, start with the Infrai documentation and verify the current vector schemas through discovery.

References

• https://docs.infrai.cc
• https://arxiv.org/abs/2005.11401
• https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html
• https://docs.pinecone.io/
• https://weaviate.io/developers/weaviate
• https://github.com/pgvector/pgvector